AI in Medical Imaging & Diagnostics: review
DOI:
https://doi.org/10.31185/wjps.752Keywords:
Artificial Intelligence (AI), Medical Diagnostics, Deep Learning, Computer-Aided Diagnosis (CAD), Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), Explainable AI (XAI), Generative Adversarial Networks (GANs), Robotic-Assisted Biopsy, Precision Oncology, Automated Blood Analyzers, Digital Pathology.Abstract
ABSTRACT: The application of artificial intelligence (AI) has transformed medical diagnostics for the better with the adoption and evolution of radiology, histopathology, clinical laboratory testing, robotic surgery, and expert systems. The implementation of deep learning algorithms using AI, such as Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and Generative Adversarial Networks (GANs), have outperformed humans in the analysis of medical images, tumor categorization, and biomarker detection. AI powered biopsy systems, which are a type of robotic assisted diagnostics, have improved the accuracy and reduced the invasiveness of the procedure, which in return, enables for better detection of diseases at earlier stages. The use of pipetting robots and automated blood analyzers for AI powered automation of clinical lab diagnostics improves the speed and volume of diagnostic assays while reducing the rate of human error. Additionally, AI powered expert systems, like OncoKB for the analysis of genetic mutations and deep mind’s AlphaFold for predicting protein structures, are advancing precision oncology and personalized medicine by offering non-biased treatment suggestions. The issues that impede the further integration of AI in diagnostics include data diversity, model generalization, obtaining legal certification, and the absence of XAI which would heighten clinician acceptance. To overcome these barriers, there needs to be standard validation frameworks, federated learning for privacy-centric AI training, and multimodal AI that incorporates medical images, genomic data, and EHRs. Emerging directions in self-supervised learning (SSL), active AI robotics, and human-machine interaction will likely improve the precision of diagnoses, automate clinical processes, and expand healthcare access around the world. This review outlines the role of AI in transforming medical diagnostics while analyzing existing obstacles, emerging possibilities, and recommendations for AI in clinical decision support systems.
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